Advances in Controlled Release Fertilizers: Cost‐Effective Coating Techniques and Smart Stimuli‐Responsive Hydrogels
Bibliographic record
Abstract
Abstract To meet the needs of a rapidly expanding global population, farmers will need more fertilizers than ever before to maintain a steady supply of affordable, nutritious food. The formulation of controlled release fertilizers (CRF) to synchronize nutrient release according to the demand of plants has emerged as a viable solution to the current problems associated with the poor nutrient usage efficiency of fertilizers. Yet, the greatest obstacle that still stands in the way of broad use of CRF in agriculture is their expensive manufacturing costs. The first section of this analysis focuses on broad topics related to CRF. Afterward, the differences between several cost‐effective raw materials and some of the production techniques used to make CRF are examined. Furthermore, the emerging field of “smart” coating materials, such as stimuli‐responsive coatings, which can accurately tailor nutrients delivery to the demands of the vegetation, is discussed, and the most important research work that could lead to their extensive use in agriculture is pointed out. The purpose of this review is to provide a strong assessment of CRF's development over the past several years by highlighting innovations and providing in‐depth analysis of prevailing patterns to better understand the future of agriculture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".